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Using Electroencephalography Measurements and High-quality Video Recording for Analyzing Visual Perception of Media Content

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EEG Signal and Feature Interaction Modeling-Based Eye Behavior Prediction Research.

Pengcheng Ma¹, Qian Gao¹

¹School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan Shandong 250353, China.

Computational and Mathematical Methods in Medicine

|June 9, 2020

Summary

This study introduces a novel approach using electroencephalogram (EEG) signals and a deep factorization machine model to predict eye states (open/closed). This method aids in diagnosing user fatigue and enhancing recommendation systems.

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Area of Science:

Neuroscience and Biomedical Engineering
Signal Processing
Machine Learning

Background:

Electroencephalogram (EEG) signal analysis is increasingly used for health monitoring.
Predicting user states like eye openness is crucial for interactive systems.

Purpose of the Study:

To analyze EEG signals for predicting eye states (open/closed) using a novel deep factorization machine model.
To explore the application of EEG data in diagnosing user fatigue and improving recommendation systems.

Main Methods:

Feature extraction from EEG data using wavelet transform.
Development of a hybrid deep factorization machine model (FM+LSTM) combining Factorization Machines and Long Short-Term Memory networks.
Testing the model's predictive performance against other classifiers on a real dataset.

Main Results:

The proposed FM+LSTM model achieved more efficient prediction results compared to existing classifier models.
The model effectively analyzes EEG data for eye state determination.
The method is applicable to acquiring interactive features, such as user fatigue.

Conclusions:

The developed deep factorization machine model offers a promising method for EEG-based eye state prediction.
This approach can significantly contribute to diagnosing user fatigue and enhancing recommendation system accuracy.
Future work may involve integrating this method with graph neural networks for advanced interactive feature analysis.